I’m working on comparing bacteria metabolic models. Each model has a set of metabolites (around 2000) and their concentration for 200 time points. I’m in the process of comparing the models to cluster them based on their similarity. One method I followed is I did a pair wise comparison for each of the metabolite pairs in two models using Euclidean distance. Below is how my data look like. This is a sample data file.
I computed pair wise Euclidean distance for Met1 from Model A and Met1 from Model B. Likewise computed the distances for all the common metabolites between the 2 models (Met4 in Model A and Met4 in Model B) and summed up the distances to get a distance (dissimilarity) between the two models. Similarly I computed the dissimilarity matrix for all the models and I used hierarchical clustering to cluster them.
As mentioned above, now I want to compute the dissimilarity of the models using Discrete Wavelet Transformation as my distance measure. I would like to know how to use Discrete Wavelet Transformation to compute a dissimilarity distance between 2 time series and hence for my models.
Previously I used DWT as a distance measure with DBSCAN for clustering metabolites in one model according to their behavior. It worked fine.